Added control loops for all subsystems and made tests run.
Change-Id: I66542db4355a89f6d24c1ad4772004182197c863
diff --git a/frc971/control_loops/python/elevator.py b/frc971/control_loops/python/elevator.py
new file mode 100755
index 0000000..2b42945
--- /dev/null
+++ b/frc971/control_loops/python/elevator.py
@@ -0,0 +1,246 @@
+#!/usr/bin/python
+
+import control_loop
+import controls
+import polytope
+import polydrivetrain
+import numpy
+import sys
+import matplotlib
+from matplotlib import pylab
+
+class Elevator(control_loop.ControlLoop):
+ def __init__(self, name="Elevator", mass=None):
+ super(Elevator, self).__init__(name)
+ # Stall Torque in N m
+ self.stall_torque = 0.476
+ # Stall Current in Amps
+ self.stall_current = 80.730
+ # Free Speed in RPM
+ self.free_speed = 13906.0
+ # Free Current in Amps
+ self.free_current = 5.820
+ # Mass of the elevator
+ if mass is None:
+ self.mass = 13.0
+ else:
+ self.mass = mass
+
+ # Resistance of the motor
+ self.R = 12.0 / self.stall_current
+ # Motor velocity constant
+ self.Kv = ((self.free_speed / 60.0 * 2.0 * numpy.pi) /
+ (12.0 - self.R * self.free_current))
+ # Torque constant
+ self.Kt = self.stall_torque / self.stall_current
+ # Gear ratio
+ self.G = (56.0 / 12.0) * (84.0 / 14.0)
+ # Pulley diameter
+ self.r = 32 * 0.005 / numpy.pi / 2.0
+ # Control loop time step
+ self.dt = 0.005
+
+ # Elevator left/right spring constant (N/m)
+ self.spring = 3000.0
+
+ # State is [average position, average velocity,
+ # position difference/2, velocity difference/2]
+ # Input is [V1, V2]
+
+ C1 = self.spring / (self.mass * 0.5)
+ C2 = self.Kt * self.G / (self.mass * 0.5 * self.r * self.R)
+ C3 = self.G * self.G * self.Kt / (
+ self.R * self.r * self.r * self.mass * 0.5 * self.Kv)
+
+ self.A_continuous = numpy.matrix(
+ [[0, 1, 0, 0],
+ [0, -C3, 0, 0],
+ [0, 0, 0, 1],
+ [0, 0, -C1 * 2.0, -C3]])
+
+ print "Full speed is", C2 / C3 * 12.0
+
+ # Start with the unmodified input
+ self.B_continuous = numpy.matrix(
+ [[0, 0],
+ [C2 / 2.0, C2 / 2.0],
+ [0, 0],
+ [C2 / 2.0, -C2 / 2.0]])
+
+ self.C = numpy.matrix([[1, 0, 1, 0],
+ [1, 0, -1, 0]])
+ self.D = numpy.matrix([[0, 0],
+ [0, 0]])
+
+ self.A, self.B = self.ContinuousToDiscrete(
+ self.A_continuous, self.B_continuous, self.dt)
+
+ print self.A
+
+ controlability = controls.ctrb(self.A, self.B);
+ print "Rank of augmented controlability matrix.", numpy.linalg.matrix_rank(
+ controlability)
+
+ q_pos = 0.02
+ q_vel = 0.400
+ q_pos_diff = 0.01
+ q_vel_diff = 0.45
+ self.Q = numpy.matrix([[(1.0 / (q_pos ** 2.0)), 0.0, 0.0, 0.0],
+ [0.0, (1.0 / (q_vel ** 2.0)), 0.0, 0.0],
+ [0.0, 0.0, (1.0 / (q_pos_diff ** 2.0)), 0.0],
+ [0.0, 0.0, 0.0, (1.0 / (q_vel_diff ** 2.0))]])
+
+ self.R = numpy.matrix([[(1.0 / (12.0 ** 2.0)), 0.0],
+ [0.0, 1.0 / (12.0 ** 2.0)]])
+ self.K = controls.dlqr(self.A, self.B, self.Q, self.R)
+ print self.K
+
+ print numpy.linalg.eig(self.A - self.B * self.K)[0]
+
+ self.rpl = 0.20
+ self.ipl = 0.05
+ self.PlaceObserverPoles([self.rpl + 1j * self.ipl,
+ self.rpl + 1j * self.ipl,
+ self.rpl - 1j * self.ipl,
+ self.rpl - 1j * self.ipl])
+
+ # The box formed by U_min and U_max must encompass all possible values,
+ # or else Austin's code gets angry.
+ self.U_max = numpy.matrix([[12.0], [12.0]])
+ self.U_min = numpy.matrix([[-12.0], [-12.0]])
+
+ self.InitializeState()
+
+
+def CapU(U):
+ if U[0, 0] - U[1, 0] > 24:
+ return numpy.matrix([[12], [-12]])
+ elif U[0, 0] - U[1, 0] < -24:
+ return numpy.matrix([[-12], [12]])
+ else:
+ max_u = max(U[0, 0], U[1, 0])
+ min_u = min(U[0, 0], U[1, 0])
+ if max_u > 12:
+ return U - (max_u - 12)
+ if min_u < -12:
+ return U - (min_u + 12)
+ return U
+
+
+def run_test(elevator, initial_X, goal, max_separation_error=0.01,
+ show_graph=True, iterations=200, controller_elevator=None,
+ observer_elevator=None):
+ """Runs the elevator plant with an initial condition and goal.
+
+ The tests themselves are not terribly sophisticated; I just test for
+ whether the goal has been reached and whether the separation goes
+ outside of the initial and goal values by more than max_separation_error.
+ Prints out something for a failure of either condition and returns
+ False if tests fail.
+ Args:
+ elevator: elevator object to use.
+ initial_X: starting state.
+ goal: goal state.
+ show_graph: Whether or not to display a graph showing the changing
+ states and voltages.
+ iterations: Number of timesteps to run the model for.
+ controller_elevator: elevator object to get K from, or None if we should
+ use elevator.
+ observer_elevator: elevator object to use for the observer, or None if we
+ should use the actual state.
+ """
+
+ elevator.X = initial_X
+
+ if controller_elevator is None:
+ controller_elevator = elevator
+
+ if observer_elevator is not None:
+ observer_elevator.X_hat = initial_X + 0.01
+ observer_elevator.X_hat = initial_X
+
+ # Various lists for graphing things.
+ t = []
+ x_avg = []
+ x_sep = []
+ x_hat_avg = []
+ x_hat_sep = []
+ v_avg = []
+ v_sep = []
+ u_left = []
+ u_right = []
+
+ sep_plot_gain = 100.0
+
+ for i in xrange(iterations):
+ X_hat = elevator.X
+ if observer_elevator is not None:
+ X_hat = observer_elevator.X_hat
+ x_hat_avg.append(observer_elevator.X_hat[0, 0])
+ x_hat_sep.append(observer_elevator.X_hat[2, 0] * sep_plot_gain)
+ U = controller_elevator.K * (goal - X_hat)
+ U = CapU(U)
+ x_avg.append(elevator.X[0, 0])
+ v_avg.append(elevator.X[1, 0])
+ x_sep.append(elevator.X[2, 0] * sep_plot_gain)
+ v_sep.append(elevator.X[3, 0])
+ if observer_elevator is not None:
+ observer_elevator.PredictObserver(U)
+ elevator.Update(U)
+ if observer_elevator is not None:
+ observer_elevator.Y = elevator.Y
+ observer_elevator.CorrectObserver(U)
+
+ t.append(i * elevator.dt)
+ u_left.append(U[0, 0])
+ u_right.append(U[1, 0])
+
+ print numpy.linalg.inv(elevator.A)
+ print "delta time is ", elevator.dt
+ print "Velocity at t=0 is ", x_avg[0], v_avg[0], x_sep[0], v_sep[0]
+ print "Velocity at t=1+dt is ", x_avg[1], v_avg[1], x_sep[1], v_sep[1]
+
+ if show_graph:
+ pylab.subplot(2, 1, 1)
+ pylab.plot(t, x_avg, label='x avg')
+ pylab.plot(t, x_sep, label='x sep')
+ if observer_elevator is not None:
+ pylab.plot(t, x_hat_avg, label='x_hat avg')
+ pylab.plot(t, x_hat_sep, label='x_hat sep')
+ pylab.legend()
+
+ pylab.subplot(2, 1, 2)
+ pylab.plot(t, u_left, label='u left')
+ pylab.plot(t, u_right, label='u right')
+ pylab.legend()
+ pylab.show()
+
+
+def main(argv):
+ loaded_mass = 25
+ #loaded_mass = 0
+ elevator = Elevator(mass=13 + loaded_mass)
+ elevator_controller = Elevator(mass=13 + 15)
+ observer_elevator = Elevator(mass=13 + 15)
+ #observer_elevator = None
+
+ # Test moving the elevator with constant separation.
+ initial_X = numpy.matrix([[0.0], [0.0], [0.01], [0.0]])
+ #initial_X = numpy.matrix([[0.0], [0.0], [0.00], [0.0]])
+ R = numpy.matrix([[1.0], [0.0], [0.0], [0.0]])
+ run_test(elevator, initial_X, R, controller_elevator=elevator_controller,
+ observer_elevator=observer_elevator)
+
+ # Write the generated constants out to a file.
+ if len(argv) != 3:
+ print "Expected .h file name and .cc file name for the elevator."
+ else:
+ elevator = Elevator("Elevator")
+ loop_writer = control_loop.ControlLoopWriter("Elevator", [elevator])
+ if argv[1][-3:] == '.cc':
+ loop_writer.Write(argv[2], argv[1])
+ else:
+ loop_writer.Write(argv[1], argv[2])
+
+if __name__ == '__main__':
+ sys.exit(main(sys.argv))